Production of Se-methylselenocysteine in transgenic plants expressing selenocysteine methyltransferase
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: It has become increasingly evident that dietary Se plays a significant role in reducing the incidence of lung, colorectal and prostate cancer in humans. Different forms of Se vary in their chemopreventative efficacy, with Se-methylselenocysteine being one of the most potent. Interestingly, the Se accumulating plant Astragalus bisulcatus (Two-grooved poison vetch) contains up to 0.6% of its shoot dry weight as Se-methylselenocysteine. The ability of this Se accumulator to biosynthesize Se-methylselenocysteine provides a critical metabolic shunt that prevents selenocysteine and selenomethionine from entering the protein biosynthetic machinery. Such a metabolic shunt has been proposed to be vital for Se tolerance in A. bisulcatus. Utilization of this mechanism in other plants may provide a possible avenue for the genetic engineering of Se tolerance in plants ideally suited for the phytoremediation of Se contaminated land. Here, we describe the overexpression of a selenocysteine methyltransferase from A. bisulcatus to engineer Se-methylselenocysteine metabolism in the Se non-accumulator Arabidopsis thaliana (Thale cress). RESULTS: By over producing the A. bisulcatus enzyme selenocysteine methyltransferase in A. thaliana, we have introduced a novel biosynthetic ability that allows the non-accumulator to accumulate Se-methylselenocysteine and gamma-glutamylmethylselenocysteine in shoots. The biosynthesis of Se-methylselenocysteine in A. thaliana also confers significantly increased selenite tolerance and foliar Se accumulation. CONCLUSION: These results demonstrate the feasibility of developing transgenic plant-based production of Se-methylselenocysteine, as well as bioengineering selenite resistance in plants. Selenite resistance is the first step in engineering plants that are resistant to selenate, the predominant form of Se in the environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it